Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations119
Missing cells78
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.0 KiB
Average record size in memory232.0 B

Variable types

Text1
Categorical2
Numeric25

Alerts

Amazonia has constant value "Sim" Constant
Consumo per capita de água is highly overall correlated with Parcela da população com acesso à água and 3 other fieldsHigh correlation
Custo com energia elétrica is highly overall correlated with Densidade demográfica and 8 other fieldsHigh correlation
Densidade demográfica is highly overall correlated with Custo com energia elétrica and 3 other fieldsHigh correlation
Estado is highly overall correlated with ID and 2 other fieldsHigh correlation
ID is highly overall correlated with EstadoHigh correlation
Incidência de internações por diarreia is highly overall correlated with Incidência de internações totais por doenças de veiculação hídricaHigh correlation
Incidência de internações totais por doenças de veiculação hídrica is highly overall correlated with Incidência de internações por diarreiaHigh correlation
Latitude is highly overall correlated with Estado and 2 other fieldsHigh correlation
Longitude is highly overall correlated with Estado and 1 other fieldsHigh correlation
Moradias sem banheiro is highly overall correlated with Latitude and 1 other fieldsHigh correlation
Parcela da população com acesso à água is highly overall correlated with Consumo per capita de água and 4 other fieldsHigh correlation
Parcela da população com coleta de esgoto is highly overall correlated with Custo com energia elétrica and 7 other fieldsHigh correlation
Parcela da população sem acesso à água is highly overall correlated with Consumo per capita de água and 4 other fieldsHigh correlation
Parcela da população sem coleta de esgoto is highly overall correlated with Custo com energia elétrica and 7 other fieldsHigh correlation
Parcela das moradias sem banheiro is highly overall correlated with Latitude and 6 other fieldsHigh correlation
População is highly overall correlated with Custo com energia elétrica and 8 other fieldsHigh correlation
População com acesso à água is highly overall correlated with Consumo per capita de água and 10 other fieldsHigh correlation
População com coleta de esgoto is highly overall correlated with Custo com energia elétrica and 7 other fieldsHigh correlation
População sem acesso à água is highly overall correlated with Consumo per capita de água and 3 other fieldsHigh correlation
População sem coleta de esgoto is highly overall correlated with Custo com energia elétrica and 3 other fieldsHigh correlation
Receita direta e indireta total is highly overall correlated with Custo com energia elétrica and 10 other fieldsHigh correlation
Área do município is highly overall correlated with Densidade demográfica and 1 other fieldsHigh correlation
Índice de esgoto tratado referido à água consumida is highly overall correlated with Custo com energia elétrica and 7 other fieldsHigh correlation
População com acesso à água has 4 (3.4%) missing values Missing
População sem acesso à água has 4 (3.4%) missing values Missing
População com coleta de esgoto has 4 (3.4%) missing values Missing
População sem coleta de esgoto has 4 (3.4%) missing values Missing
Parcela da população sem acesso à água has 4 (3.4%) missing values Missing
Parcela da população com acesso à água has 4 (3.4%) missing values Missing
Parcela da população sem coleta de esgoto has 4 (3.4%) missing values Missing
Parcela da população com coleta de esgoto has 4 (3.4%) missing values Missing
Receita direta e indireta total has 14 (11.8%) missing values Missing
Consumo per capita de água has 4 (3.4%) missing values Missing
Perdas na distribuição has 4 (3.4%) missing values Missing
Índice de esgoto tratado referido à água consumida has 4 (3.4%) missing values Missing
Tarifa de água has 14 (11.8%) missing values Missing
Custo com energia elétrica has 6 (5.0%) missing values Missing
Cidade has unique values Unique
ID has unique values Unique
Latitude has unique values Unique
Longitude has unique values Unique
População has unique values Unique
Área do município has unique values Unique
População sem acesso à água has 6 (5.0%) zeros Zeros
População com coleta de esgoto has 60 (50.4%) zeros Zeros
Parcela da população sem acesso à água has 6 (5.0%) zeros Zeros
Parcela da população com coleta de esgoto has 60 (50.4%) zeros Zeros
Incidência de internações por diarreia has 2 (1.7%) zeros Zeros
Taxa de óbitos por doenças de veiculação hídrica has 55 (46.2%) zeros Zeros
Índice de esgoto tratado referido à água consumida has 69 (58.0%) zeros Zeros
Incidência de internações totais por doenças de veiculação hídrica has 2 (1.7%) zeros Zeros

Reproduction

Analysis started2025-06-06 03:31:26.669973
Analysis finished2025-06-06 03:32:36.264950
Duration1 minute and 9.59 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Cidade
Text

Unique 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:36.459526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length27
Median length19
Mean length10.411765
Min length4

Characters and Unicode

Total characters1239
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique119 ?
Unique (%)100.0%

Sample

1st rowCruzeiro do Sul
2nd rowRio Branco
3rd rowMacapá
4th rowSantana
5th rowCareiro da Várzea
ValueCountFrequency (%)
do 16
 
8.1%
pará 4
 
2.0%
santa 4
 
2.0%
da 4
 
2.0%
são 4
 
2.0%
rio 3
 
1.5%
de 3
 
1.5%
grande 2
 
1.0%
várzea 2
 
1.0%
santana 2
 
1.0%
Other values (148) 153
77.7%
2025-06-06T00:32:36.878562image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 184
14.9%
r 96
 
7.7%
o 94
 
7.6%
78
 
6.3%
i 77
 
6.2%
e 71
 
5.7%
n 67
 
5.4%
u 49
 
4.0%
t 46
 
3.7%
d 44
 
3.6%
Other values (48) 433
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1239
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 184
14.9%
r 96
 
7.7%
o 94
 
7.6%
78
 
6.3%
i 77
 
6.2%
e 71
 
5.7%
n 67
 
5.4%
u 49
 
4.0%
t 46
 
3.7%
d 44
 
3.6%
Other values (48) 433
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1239
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 184
14.9%
r 96
 
7.7%
o 94
 
7.6%
78
 
6.3%
i 77
 
6.2%
e 71
 
5.7%
n 67
 
5.4%
u 49
 
4.0%
t 46
 
3.7%
d 44
 
3.6%
Other values (48) 433
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1239
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 184
14.9%
r 96
 
7.7%
o 94
 
7.6%
78
 
6.3%
i 77
 
6.2%
e 71
 
5.7%
n 67
 
5.4%
u 49
 
4.0%
t 46
 
3.7%
d 44
 
3.6%
Other values (48) 433
34.9%

Estado
Categorical

High correlation 

Distinct9
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Pará
46 
Maranhão
21 
Mato Grosso
20 
Amazonas
15 
Rondônia
Other values (4)
10 

Length

Max length11
Median length9
Mean length6.8739496
Min length4

Characters and Unicode

Total characters818
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowAcre
2nd rowAcre
3rd rowAmapá
4th rowAmapá
5th rowAmazonas

Common Values

ValueCountFrequency (%)
Pará 46
38.7%
Maranhão 21
17.6%
Mato Grosso 20
16.8%
Amazonas 15
 
12.6%
Rondônia 7
 
5.9%
Tocantins 5
 
4.2%
Acre 2
 
1.7%
Amapá 2
 
1.7%
Roraima 1
 
0.8%

Length

2025-06-06T00:32:37.047377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-06T00:32:37.205325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pará 46
33.1%
maranhão 21
15.1%
mato 20
14.4%
grosso 20
14.4%
amazonas 15
 
10.8%
rondônia 7
 
5.0%
tocantins 5
 
3.6%
acre 2
 
1.4%
amapá 2
 
1.4%
roraima 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 154
18.8%
o 109
13.3%
r 90
11.0%
s 60
 
7.3%
n 60
 
7.3%
á 48
 
5.9%
P 46
 
5.6%
M 41
 
5.0%
t 25
 
3.1%
ã 21
 
2.6%
Other values (14) 164
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 154
18.8%
o 109
13.3%
r 90
11.0%
s 60
 
7.3%
n 60
 
7.3%
á 48
 
5.9%
P 46
 
5.6%
M 41
 
5.0%
t 25
 
3.1%
ã 21
 
2.6%
Other values (14) 164
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 154
18.8%
o 109
13.3%
r 90
11.0%
s 60
 
7.3%
n 60
 
7.3%
á 48
 
5.9%
P 46
 
5.6%
M 41
 
5.0%
t 25
 
3.1%
ã 21
 
2.6%
Other values (14) 164
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 154
18.8%
o 109
13.3%
r 90
11.0%
s 60
 
7.3%
n 60
 
7.3%
á 48
 
5.9%
P 46
 
5.6%
M 41
 
5.0%
t 25
 
3.1%
ã 21
 
2.6%
Other values (14) 164
20.0%

ID
Real number (ℝ)

High correlation  Unique 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217104.87
Minimum110002
Maximum510840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:37.386183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum110002
5-th percentile110029.8
Q1150105
median150635
Q3210645
95-th percentile510761
Maximum510840
Range400838
Interquartile range (IQR)60540

Descriptive statistics

Standard deviation135382.48
Coefficient of variation (CV)0.62358102
Kurtosis0.93660131
Mean217104.87
Median Absolute Deviation (MAD)20375
Skewness1.6332655
Sum25835480
Variance1.8328415 × 1010
MonotonicityNot monotonic
2025-06-06T00:32:37.568484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120020 1
 
0.8%
150293 1
 
0.8%
150510 1
 
0.8%
150506 1
 
0.8%
150480 1
 
0.8%
150470 1
 
0.8%
150442 1
 
0.8%
150420 1
 
0.8%
150390 1
 
0.8%
150380 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
110002 1
0.8%
110004 1
0.8%
110011 1
0.8%
110012 1
0.8%
110020 1
0.8%
110028 1
0.8%
110030 1
0.8%
120020 1
0.8%
120040 1
0.8%
130115 1
0.8%
ValueCountFrequency (%)
510840 1
0.8%
510795 1
0.8%
510792 1
0.8%
510790 1
0.8%
510780 1
0.8%
510770 1
0.8%
510760 1
0.8%
510704 1
0.8%
510650 1
0.8%
510645 1
0.8%

Latitude
Real number (ℝ)

High correlation  Unique 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.9333958
Minimum-16.460353
Maximum2.8208478
Zeros0
Zeros (%)0.0%
Negative117
Negative (%)98.3%
Memory size1.9 KiB
2025-06-06T00:32:37.740848image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-16.460353
5-th percentile-15.781176
Q1-9.8887534
median-3.7785073
Q3-2.3257243
95-th percentile-1.1931941
Maximum2.8208478
Range19.281201
Interquartile range (IQR)7.5630291

Descriptive statistics

Standard deviation4.9544457
Coefficient of variation (CV)-0.83501016
Kurtosis-0.48309566
Mean-5.9333958
Median Absolute Deviation (MAD)2.0337911
Skewness-0.90488168
Sum-706.07409
Variance24.546532
MonotonicityNot monotonic
2025-06-06T00:32:37.914511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.6362478 1
 
0.8%
-4.2928111 1
 
0.8%
-1.901072 1
 
0.8%
-4.2519172 1
 
0.8%
-1.9989108 1
 
0.8%
-1.889926 1
 
0.8%
-1.3595306 1
 
0.8%
-5.3462821 1
 
0.8%
-2.1571381 1
 
0.8%
-4.446173 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
-16.4603533 1
0.8%
-16.2578606 1
0.8%
-16.1956788 1
0.8%
-16.0644068 1
0.8%
-15.8916033 1
0.8%
-15.859843 1
0.8%
-15.7724354 1
0.8%
-15.645816 1
0.8%
-15.5986686 1
0.8%
-15.5614127 1
0.8%
ValueCountFrequency (%)
2.8208478 1
0.8%
0.0401529 1
0.8%
-0.0307068 1
0.8%
-0.8546027 1
0.8%
-1.0574255 1
0.8%
-1.1921927 1
0.8%
-1.1933054 1
0.8%
-1.204892 1
0.8%
-1.2927031 1
0.8%
-1.2968557 1
0.8%

Longitude
Real number (ℝ)

High correlation  Unique 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-52.506773
Minimum-72.669165
Maximum-43.892354
Zeros0
Zeros (%)0.0%
Negative119
Negative (%)100.0%
Memory size1.9 KiB
2025-06-06T00:32:38.081976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-72.669165
5-th percentile-63.044074
Q1-56.383535
median-50.030954
Q3-47.739149
95-th percentile-44.282674
Maximum-43.892354
Range28.776811
Interquartile range (IQR)8.6443858

Descriptive statistics

Standard deviation6.358482
Coefficient of variation (CV)-0.12109832
Kurtosis-0.083008164
Mean-52.506773
Median Absolute Deviation (MAD)4.7074007
Skewness-0.72348281
Sum-6248.306
Variance40.430293
MonotonicityNot monotonic
2025-06-06T00:32:38.250360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-72.6691649 1
 
0.8%
-47.5559083 1
 
0.8%
-55.520812 1
 
0.8%
-49.9524257 1
 
0.8%
-54.0727694 1
 
0.8%
-48.766765 1
 
0.8%
-48.3361504 1
 
0.8%
-49.1007401 1
 
0.8%
-56.0900977 1
 
0.8%
-49.115272 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
-72.6691649 1
0.8%
-69.9263372 1
0.8%
-67.8220778 1
0.8%
-64.7102278 1
0.8%
-63.8735438 1
0.8%
-63.1431166 1
0.8%
-63.033069 1
0.8%
-63.0248674 1
0.8%
-62.478769 1
0.8%
-61.9277854 1
0.8%
ValueCountFrequency (%)
-43.892354 1
0.8%
-43.9149128 1
0.8%
-44.0559509 1
0.8%
-44.1104256 1
0.8%
-44.1360269 1
0.8%
-44.1591965 1
0.8%
-44.2963942 1
0.8%
-44.3560724 1
0.8%
-44.4174143 1
0.8%
-44.781167 1
0.8%

Amazonia
Categorical

Constant 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Sim
119 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters357
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSim
2nd rowSim
3rd rowSim
4th rowSim
5th rowSim

Common Values

ValueCountFrequency (%)
Sim 119
100.0%

Length

2025-06-06T00:32:38.405915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-06T00:32:38.514010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
sim 119
100.0%

Most occurring characters

ValueCountFrequency (%)
S 119
33.3%
i 119
33.3%
m 119
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 119
33.3%
i 119
33.3%
m 119
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 119
33.3%
i 119
33.3%
m 119
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 119
33.3%
i 119
33.3%
m 119
33.3%

População
Real number (ℝ)

High correlation  Unique 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140483.91
Minimum3166
Maximum2063689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:38.645305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3166
5-th percentile15432.3
Q152627
median70616
Q3111210
95-th percentile444683.1
Maximum2063689
Range2060523
Interquartile range (IQR)58583

Descriptive statistics

Standard deviation249611.47
Coefficient of variation (CV)1.7767976
Kurtosis34.306177
Mean140483.91
Median Absolute Deviation (MAD)31151
Skewness5.3398935
Sum16717585
Variance6.2305888 × 1010
MonotonicityNot monotonic
2025-06-06T00:32:38.814083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91888 1
 
0.8%
58484 1
 
0.8%
52229 1
 
0.8%
60732 1
 
0.8%
60012 1
 
0.8%
84094 1
 
0.8%
111785 1
 
0.8%
266533 1
 
0.8%
50881 1
 
0.8%
37707 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
3166 1
0.8%
3932 1
0.8%
7253 1
0.8%
7426 1
0.8%
12940 1
0.8%
15246 1
0.8%
15453 1
0.8%
15761 1
0.8%
18467 1
0.8%
18990 1
0.8%
ValueCountFrequency (%)
2063689 1
0.8%
1303403 1
0.8%
1037775 1
0.8%
650877 1
0.8%
478778 1
0.8%
460434 1
0.8%
442933 1
0.8%
413486 1
0.8%
364756 1
0.8%
331942 1
0.8%

População com acesso à água
Real number (ℝ)

High correlation  Missing 

Distinct115
Distinct (%)100.0%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean104387.9
Minimum549
Maximum2053105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:39.053276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum549
5-th percentile5067.4
Q117707.5
median46566
Q390972
95-th percentile274362.4
Maximum2053105
Range2052556
Interquartile range (IQR)73264.5

Descriptive statistics

Standard deviation245132.84
Coefficient of variation (CV)2.348288
Kurtosis39.834066
Mean104387.9
Median Absolute Deviation (MAD)33882
Skewness5.8706041
Sum12004608
Variance6.009011 × 1010
MonotonicityNot monotonic
2025-06-06T00:32:39.221604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46566 1
 
0.8%
29100 1
 
0.8%
68294 1
 
0.8%
18415 1
 
0.8%
4653 1
 
0.8%
10919 1
 
0.8%
12694 1
 
0.8%
54760 1
 
0.8%
107270 1
 
0.8%
25741 1
 
0.8%
Other values (105) 105
88.2%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
549 1
0.8%
2614 1
0.8%
2770 1
0.8%
2945 1
0.8%
3267 1
0.8%
4653 1
0.8%
5245 1
0.8%
5281 1
0.8%
5953 1
0.8%
6316 1
0.8%
ValueCountFrequency (%)
2053105 1
0.8%
1244965 1
0.8%
962616 1
0.8%
650877 1
0.8%
398806 1
0.8%
296432 1
0.8%
264904 1
0.8%
244911 1
0.8%
240857 1
0.8%
228975 1
0.8%

População sem acesso à água
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct110
Distinct (%)95.7%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean40105.017
Minimum0
Maximum274155
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:39.376525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.3
Q17934.5
median19088
Q353046.5
95-th percentile162365.3
Maximum274155
Range274155
Interquartile range (IQR)45112

Descriptive statistics

Standard deviation52645.791
Coefficient of variation (CV)1.3126984
Kurtosis6.6536399
Mean40105.017
Median Absolute Deviation (MAD)17581
Skewness2.440109
Sum4612077
Variance2.7715793 × 109
MonotonicityNot monotonic
2025-06-06T00:32:39.536758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.0%
45322 1
 
0.8%
1229 1
 
0.8%
33814 1
 
0.8%
56079 1
 
0.8%
49093 1
 
0.8%
71400 1
 
0.8%
57025 1
 
0.8%
159263 1
 
0.8%
25140 1
 
0.8%
Other values (100) 100
84.0%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0 6
5.0%
59 1
 
0.8%
233 1
 
0.8%
235 1
 
0.8%
278 1
 
0.8%
372 1
 
0.8%
479 1
 
0.8%
987 1
 
0.8%
1074 1
 
0.8%
1229 1
 
0.8%
ValueCountFrequency (%)
274155 1
0.8%
268010 1
0.8%
202076 1
0.8%
194008 1
0.8%
169949 1
0.8%
169604 1
0.8%
159263 1
0.8%
141672 1
0.8%
131224 1
0.8%
130041 1
0.8%

População com coleta de esgoto
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct56
Distinct (%)48.7%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean36019.27
Minimum0
Maximum563330
Zeros60
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:39.693501image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317518
95-th percentile249154.2
Maximum563330
Range563330
Interquartile range (IQR)17518

Descriptive statistics

Standard deviation100175.22
Coefficient of variation (CV)2.7811564
Kurtosis15.853316
Mean36019.27
Median Absolute Deviation (MAD)0
Skewness3.9272573
Sum4142216
Variance1.0035075 × 1010
MonotonicityNot monotonic
2025-06-06T00:32:39.850573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
50.4%
26443 1
 
0.8%
259055 1
 
0.8%
900 1
 
0.8%
1492 1
 
0.8%
330 1
 
0.8%
818 1
 
0.8%
2090 1
 
0.8%
15464 1
 
0.8%
2476 1
 
0.8%
Other values (46) 46
38.7%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0 60
50.4%
130 1
 
0.8%
330 1
 
0.8%
818 1
 
0.8%
900 1
 
0.8%
1110 1
 
0.8%
1415 1
 
0.8%
1425 1
 
0.8%
1492 1
 
0.8%
1581 1
 
0.8%
ValueCountFrequency (%)
563330 1
0.8%
538324 1
0.8%
490314 1
0.8%
383731 1
0.8%
272301 1
0.8%
259055 1
0.8%
244911 1
0.8%
175338 1
0.8%
98720 1
0.8%
87234 1
0.8%

População sem coleta de esgoto
Real number (ℝ)

High correlation  Missing 

Distinct115
Distinct (%)100.0%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean108473.64
Minimum0
Maximum1525365
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:40.013741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15606.5
Q144036.5
median65418
Q398324
95-th percentile308201
Maximum1525365
Range1525365
Interquartile range (IQR)54287.5

Descriptive statistics

Standard deviation180556.72
Coefficient of variation (CV)1.6645216
Kurtosis39.024655
Mean108473.64
Median Absolute Deviation (MAD)30414
Skewness5.729528
Sum12474469
Variance3.2600729 × 1010
MonotonicityNot monotonic
2025-06-06T00:32:40.195393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91888 1
 
0.8%
30329 1
 
0.8%
68294 1
 
0.8%
52229 1
 
0.8%
58256 1
 
0.8%
60012 1
 
0.8%
84094 1
 
0.8%
96321 1
 
0.8%
264443 1
 
0.8%
50881 1
 
0.8%
Other values (105) 105
88.2%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0 1
0.8%
3932 1
0.8%
7253 1
0.8%
7426 1
0.8%
12940 1
0.8%
15246 1
0.8%
15761 1
0.8%
16784 1
0.8%
18467 1
0.8%
18962 1
0.8%
ValueCountFrequency (%)
1525365 1
0.8%
1044348 1
0.8%
474445 1
0.8%
414909 1
0.8%
407273 1
0.8%
319310 1
0.8%
303440 1
0.8%
289373 1
0.8%
264443 1
0.8%
234952 1
0.8%

Parcela da população sem acesso à água
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct100
Distinct (%)87.0%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.38885217
Minimum0
Maximum0.972
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:40.373400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0014
Q10.1115
median0.364
Q30.642
95-th percentile0.8734
Maximum0.972
Range0.972
Interquartile range (IQR)0.5305

Descriptive statistics

Standard deviation0.31204299
Coefficient of variation (CV)0.80247203
Kurtosis-1.3500803
Mean0.38885217
Median Absolute Deviation (MAD)0.265
Skewness0.28956001
Sum44.718
Variance0.097370829
MonotonicityNot monotonic
2025-06-06T00:32:40.552165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.0%
0.836 2
 
1.7%
0.506 2
 
1.7%
0.045 2
 
1.7%
0.136 2
 
1.7%
0.004 2
 
1.7%
0.003 2
 
1.7%
0.005 2
 
1.7%
0.549 2
 
1.7%
0.582 2
 
1.7%
Other values (90) 91
76.5%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0 6
5.0%
0.002 1
 
0.8%
0.003 2
 
1.7%
0.004 2
 
1.7%
0.005 2
 
1.7%
0.012 1
 
0.8%
0.021 1
 
0.8%
0.023 1
 
0.8%
0.033 1
 
0.8%
0.036 1
 
0.8%
ValueCountFrequency (%)
0.972 1
0.8%
0.96 1
0.8%
0.923 1
0.8%
0.919 1
0.8%
0.899 1
0.8%
0.879 1
0.8%
0.871 1
0.8%
0.867 1
0.8%
0.857 1
0.8%
0.849 1
0.8%

Parcela da população com acesso à água
Real number (ℝ)

High correlation  Missing 

Distinct100
Distinct (%)87.0%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.61114783
Minimum0.028
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:40.723251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.028
5-th percentile0.1266
Q10.358
median0.636
Q30.8885
95-th percentile0.9986
Maximum1
Range0.972
Interquartile range (IQR)0.5305

Descriptive statistics

Standard deviation0.31204299
Coefficient of variation (CV)0.51058513
Kurtosis-1.3500803
Mean0.61114783
Median Absolute Deviation (MAD)0.265
Skewness-0.28956001
Sum70.282
Variance0.097370829
MonotonicityNot monotonic
2025-06-06T00:32:40.900043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
5.0%
0.164 2
 
1.7%
0.494 2
 
1.7%
0.955 2
 
1.7%
0.864 2
 
1.7%
0.996 2
 
1.7%
0.997 2
 
1.7%
0.995 2
 
1.7%
0.451 2
 
1.7%
0.418 2
 
1.7%
Other values (90) 91
76.5%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0.028 1
0.8%
0.04 1
0.8%
0.077 1
0.8%
0.081 1
0.8%
0.101 1
0.8%
0.121 1
0.8%
0.129 1
0.8%
0.133 1
0.8%
0.143 1
0.8%
0.151 1
0.8%
ValueCountFrequency (%)
1 6
5.0%
0.998 1
 
0.8%
0.997 2
 
1.7%
0.996 2
 
1.7%
0.995 2
 
1.7%
0.988 1
 
0.8%
0.979 1
 
0.8%
0.977 1
 
0.8%
0.967 1
 
0.8%
0.964 1
 
0.8%

Parcela da população sem coleta de esgoto
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)44.3%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.87222609
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:41.072517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3251
Q10.8695
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1305

Descriptive statistics

Standard deviation0.23347978
Coefficient of variation (CV)0.26768264
Kurtosis3.6802447
Mean0.87222609
Median Absolute Deviation (MAD)0
Skewness-2.0989482
Sum100.306
Variance0.054512808
MonotonicityNot monotonic
2025-06-06T00:32:41.323027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 60
50.4%
0.919 2
 
1.7%
0.791 2
 
1.7%
0.992 2
 
1.7%
0.994 2
 
1.7%
0.975 2
 
1.7%
0.801 1
 
0.8%
0.98 1
 
0.8%
0.984 1
 
0.8%
0.862 1
 
0.8%
Other values (41) 41
34.5%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0 1
0.8%
0.072 1
0.8%
0.1 1
0.8%
0.197 1
0.8%
0.247 1
0.8%
0.274 1
0.8%
0.347 1
0.8%
0.37 1
0.8%
0.373 1
0.8%
0.451 1
0.8%
ValueCountFrequency (%)
1 60
50.4%
0.994 2
 
1.7%
0.992 2
 
1.7%
0.987 1
 
0.8%
0.986 1
 
0.8%
0.985 1
 
0.8%
0.984 1
 
0.8%
0.98 1
 
0.8%
0.975 2
 
1.7%
0.971 1
 
0.8%

Parcela da população com coleta de esgoto
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct51
Distinct (%)44.3%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.12777391
Minimum0
Maximum1
Zeros60
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:41.494298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1305
95-th percentile0.6749
Maximum1
Range1
Interquartile range (IQR)0.1305

Descriptive statistics

Standard deviation0.23347978
Coefficient of variation (CV)1.8272883
Kurtosis3.6802447
Mean0.12777391
Median Absolute Deviation (MAD)0
Skewness2.0989482
Sum14.694
Variance0.054512808
MonotonicityNot monotonic
2025-06-06T00:32:41.670828image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
50.4%
0.081 2
 
1.7%
0.209 2
 
1.7%
0.008 2
 
1.7%
0.006 2
 
1.7%
0.025 2
 
1.7%
0.199 1
 
0.8%
0.02 1
 
0.8%
0.016 1
 
0.8%
0.138 1
 
0.8%
Other values (41) 41
34.5%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0 60
50.4%
0.006 2
 
1.7%
0.008 2
 
1.7%
0.013 1
 
0.8%
0.014 1
 
0.8%
0.015 1
 
0.8%
0.016 1
 
0.8%
0.02 1
 
0.8%
0.025 2
 
1.7%
0.029 1
 
0.8%
ValueCountFrequency (%)
1 1
0.8%
0.928 1
0.8%
0.9 1
0.8%
0.803 1
0.8%
0.753 1
0.8%
0.726 1
0.8%
0.653 1
0.8%
0.63 1
0.8%
0.627 1
0.8%
0.549 1
0.8%

Densidade demográfica
Real number (ℝ)

High correlation 

Distinct117
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.51454
Minimum0.42
Maximum2513.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:41.838140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile1.192
Q16.22
median16.36
Q347
95-th percentile481.395
Maximum2513.92
Range2513.5
Interquartile range (IQR)40.78

Descriptive statistics

Standard deviation315.3893
Coefficient of variation (CV)3.0468117
Kurtosis32.371418
Mean103.51454
Median Absolute Deviation (MAD)13.74
Skewness5.27215
Sum12318.23
Variance99470.412
MonotonicityNot monotonic
2025-06-06T00:32:42.006492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.29 2
 
1.7%
1.3 2
 
1.7%
186.86 1
 
0.8%
3.94 1
 
0.8%
3.31 1
 
0.8%
9.25 1
 
0.8%
1081.69 1
 
0.8%
17.62 1
 
0.8%
6.13 1
 
0.8%
18.78 1
 
0.8%
Other values (107) 107
89.9%
ValueCountFrequency (%)
0.42 1
0.8%
0.63 1
0.8%
0.65 1
0.8%
0.78 1
0.8%
0.79 1
0.8%
1.12 1
0.8%
1.2 1
0.8%
1.21 1
0.8%
1.22 1
0.8%
1.3 2
1.7%
ValueCountFrequency (%)
2513.92 1
0.8%
1243.1 1
0.8%
1230.25 1
0.8%
1162.73 1
0.8%
1081.69 1
0.8%
633.99 1
0.8%
464.44 1
0.8%
338.44 1
0.8%
286.28 1
0.8%
199.5 1
0.8%

Área do município
Real number (ℝ)

High correlation  Unique 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11266.424
Minimum66.4
Maximum159533.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:42.166459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum66.4
5-th percentile375.0147
Q11977.2705
median4837.038
Q39455.2815
95-th percentile40823.462
Maximum159533.33
Range159466.93
Interquartile range (IQR)7478.011

Descriptive statistics

Standard deviation21065.454
Coefficient of variation (CV)1.8697552
Kurtosis24.698107
Mean11266.424
Median Absolute Deviation (MAD)3468.05
Skewness4.4776342
Sum1340704.5
Variance4.4375337 × 108
MonotonicityNot monotonic
2025-06-06T00:32:42.339295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8779.403 1
 
0.8%
5268.815 1
 
0.8%
28021.443 1
 
0.8%
15398.716 1
 
0.8%
18152.559 1
 
0.8%
9094.139 1
 
0.8%
103.343 1
 
0.8%
15128.058 1
 
0.8%
8305.128 1
 
0.8%
2008.315 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
66.4 1
0.8%
103.343 1
0.8%
125.259 1
0.8%
187.826 1
0.8%
190.451 1
0.8%
278.154 1
0.8%
385.777 1
0.8%
539.079 1
0.8%
614.693 1
0.8%
717.662 1
0.8%
ValueCountFrequency (%)
159533.328 1
0.8%
107603.661 1
0.8%
84212.847 1
0.8%
62042.472 1
0.8%
57970.768 1
0.8%
48315.022 1
0.8%
39991.067 1
0.8%
37805.257 1
0.8%
34090.962 1
0.8%
33111.164 1
0.8%

Receita direta e indireta total
Real number (ℝ)

High correlation  Missing 

Distinct105
Distinct (%)100.0%
Missing14
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean36108355
Minimum5230
Maximum7.3473992 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:42.511491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5230
5-th percentile736299.44
Q12295085.4
median6610780.7
Q323263059
95-th percentile1.258539 × 108
Maximum7.3473992 × 108
Range7.3473469 × 108
Interquartile range (IQR)20967974

Descriptive statistics

Standard deviation96179343
Coefficient of variation (CV)2.6636312
Kurtosis29.896141
Mean36108355
Median Absolute Deviation (MAD)5690275.4
Skewness5.0663913
Sum3.7913773 × 109
Variance9.250466 × 1015
MonotonicityNot monotonic
2025-06-06T00:32:42.688161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5295069.28 1
 
0.8%
14507107.31 1
 
0.8%
4002201.53 1
 
0.8%
1602063.95 1
 
0.8%
2515162.57 1
 
0.8%
2781960.81 1
 
0.8%
14409522.11 1
 
0.8%
26016882.44 1
 
0.8%
2704049.42 1
 
0.8%
1745171.08 1
 
0.8%
Other values (95) 95
79.8%
(Missing) 14
 
11.8%
ValueCountFrequency (%)
5230 1
0.8%
309691.61 1
0.8%
456691.2 1
0.8%
611812.58 1
0.8%
649626.63 1
0.8%
722235.35 1
0.8%
792555.8 1
0.8%
906661.77 1
0.8%
909202 1
0.8%
920505.29 1
0.8%
ValueCountFrequency (%)
734739921.7 1
0.8%
382263684.6 1
0.8%
370906731.6 1
0.8%
350055595.9 1
0.8%
247949577.4 1
0.8%
129449742.3 1
0.8%
111470545.9 1
0.8%
93576361.94 1
0.8%
88642523.59 1
0.8%
76151897.01 1
0.8%

Parcela das moradias sem banheiro
Real number (ℝ)

High correlation 

Distinct109
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.086640336
Minimum0.0006
Maximum0.4731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:42.861449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0006
5-th percentile0.00176
Q10.00995
median0.0466
Q30.13615
95-th percentile0.27177
Maximum0.4731
Range0.4725
Interquartile range (IQR)0.1262

Descriptive statistics

Standard deviation0.094673075
Coefficient of variation (CV)1.0927136
Kurtosis1.6359518
Mean0.086640336
Median Absolute Deviation (MAD)0.043
Skewness1.3312254
Sum10.3102
Variance0.0089629911
MonotonicityNot monotonic
2025-06-06T00:32:43.029850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0068 3
 
2.5%
0.0127 2
 
1.7%
0.0413 2
 
1.7%
0.0258 2
 
1.7%
0.1336 2
 
1.7%
0.0075 2
 
1.7%
0.0024 2
 
1.7%
0.0018 2
 
1.7%
0.0013 2
 
1.7%
0.0481 1
 
0.8%
Other values (99) 99
83.2%
ValueCountFrequency (%)
0.0006 1
0.8%
0.001 1
0.8%
0.0011 1
0.8%
0.0013 2
1.7%
0.0014 1
0.8%
0.0018 2
1.7%
0.0019 1
0.8%
0.0022 1
0.8%
0.0023 1
0.8%
0.0024 2
1.7%
ValueCountFrequency (%)
0.4731 1
0.8%
0.345 1
0.8%
0.3204 1
0.8%
0.2934 1
0.8%
0.292 1
0.8%
0.276 1
0.8%
0.2713 1
0.8%
0.2593 1
0.8%
0.2402 1
0.8%
0.24 1
0.8%

Incidência de internações por diarreia
Real number (ℝ)

High correlation  Zeros 

Distinct117
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.134134
Minimum0
Maximum181.24
Zeros2
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:43.190235image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9811
Q12.8765
median6.365
Q312.7445
95-th percentile49.4441
Maximum181.24
Range181.24
Interquartile range (IQR)9.868

Descriptive statistics

Standard deviation24.551756
Coefficient of variation (CV)1.7370541
Kurtosis21.108447
Mean14.134134
Median Absolute Deviation (MAD)4.264
Skewness4.1238761
Sum1681.962
Variance602.7887
MonotonicityNot monotonic
2025-06-06T00:32:43.359251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.466 2
 
1.7%
0 2
 
1.7%
4.135 1
 
0.8%
181.24 1
 
0.8%
28.72 1
 
0.8%
5.928 1
 
0.8%
9.831 1
 
0.8%
9.87 1
 
0.8%
3.847 1
 
0.8%
1.013 1
 
0.8%
Other values (107) 107
89.9%
ValueCountFrequency (%)
0 2
1.7%
0.362 1
0.8%
0.408 1
0.8%
0.656 1
0.8%
0.694 1
0.8%
1.013 1
0.8%
1.03 1
0.8%
1.053 1
0.8%
1.122 1
0.8%
1.206 1
0.8%
ValueCountFrequency (%)
181.24 1
0.8%
105.51 1
0.8%
103.306 1
0.8%
100.182 1
0.8%
70.034 1
0.8%
54.629 1
0.8%
48.868 1
0.8%
48.267 1
0.8%
41.062 1
0.8%
38.056 1
0.8%
Distinct64
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12671429
Minimum0
Maximum0.721
Zeros55
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:43.527162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.069
Q30.192
95-th percentile0.5286
Maximum0.721
Range0.721
Interquartile range (IQR)0.192

Descriptive statistics

Standard deviation0.1711352
Coefficient of variation (CV)1.3505596
Kurtosis2.8686783
Mean0.12671429
Median Absolute Deviation (MAD)0.069
Skewness1.7497233
Sum15.079
Variance0.029287257
MonotonicityNot monotonic
2025-06-06T00:32:43.772568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
46.2%
0.104 2
 
1.7%
0.109 1
 
0.8%
0.15 1
 
0.8%
0.16 1
 
0.8%
0.224 1
 
0.8%
0.333 1
 
0.8%
0.238 1
 
0.8%
0.268 1
 
0.8%
0.617 1
 
0.8%
Other values (54) 54
45.4%
ValueCountFrequency (%)
0 55
46.2%
0.046 1
 
0.8%
0.055 1
 
0.8%
0.058 1
 
0.8%
0.065 1
 
0.8%
0.069 1
 
0.8%
0.077 1
 
0.8%
0.079 1
 
0.8%
0.088 1
 
0.8%
0.094 1
 
0.8%
ValueCountFrequency (%)
0.721 1
0.8%
0.694 1
0.8%
0.654 1
0.8%
0.647 1
0.8%
0.617 1
0.8%
0.588 1
0.8%
0.522 1
0.8%
0.513 1
0.8%
0.444 1
0.8%
0.426 1
0.8%

Consumo per capita de água
Real number (ℝ)

High correlation  Missing 

Distinct115
Distinct (%)100.0%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean113.53591
Minimum5.04
Maximum722.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:43.941222image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5.04
5-th percentile14.665
Q143.38
median99.17
Q3146.03
95-th percentile307.387
Maximum722.67
Range717.63
Interquartile range (IQR)102.65

Descriptive statistics

Standard deviation107.19074
Coefficient of variation (CV)0.94411308
Kurtosis11.693805
Mean113.53591
Median Absolute Deviation (MAD)52.36
Skewness2.8158824
Sum13056.63
Variance11489.855
MonotonicityNot monotonic
2025-06-06T00:32:44.113289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 1
 
0.8%
722.67 1
 
0.8%
352.19 1
 
0.8%
43.73 1
 
0.8%
8.65 1
 
0.8%
22.61 1
 
0.8%
16.91 1
 
0.8%
65.89 1
 
0.8%
52.12 1
 
0.8%
107.76 1
 
0.8%
Other values (105) 105
88.2%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
5.04 1
0.8%
5.78 1
0.8%
7.1 1
0.8%
8.65 1
0.8%
10.27 1
0.8%
12.25 1
0.8%
15.7 1
0.8%
15.8 1
0.8%
16.91 1
0.8%
18.13 1
0.8%
ValueCountFrequency (%)
722.67 1
0.8%
596.42 1
0.8%
375.11 1
0.8%
352.57 1
0.8%
352.19 1
0.8%
333 1
0.8%
296.41 1
0.8%
293.56 1
0.8%
250.24 1
0.8%
225.39 1
0.8%

Perdas na distribuição
Real number (ℝ)

Missing 

Distinct105
Distinct (%)91.3%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.41734783
Minimum0.048
Maximum0.916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:44.282348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.048
5-th percentile0.1065
Q10.2725
median0.415
Q30.5565
95-th percentile0.7133
Maximum0.916
Range0.868
Interquartile range (IQR)0.284

Descriptive statistics

Standard deviation0.19657653
Coefficient of variation (CV)0.47101368
Kurtosis-0.62287797
Mean0.41734783
Median Absolute Deviation (MAD)0.144
Skewness0.075367647
Sum47.995
Variance0.038642334
MonotonicityNot monotonic
2025-06-06T00:32:44.445047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.418 3
 
2.5%
0.276 2
 
1.7%
0.7 2
 
1.7%
0.351 2
 
1.7%
0.113 2
 
1.7%
0.529 2
 
1.7%
0.223 2
 
1.7%
0.42 2
 
1.7%
0.532 2
 
1.7%
0.327 1
 
0.8%
Other values (95) 95
79.8%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0.048 1
0.8%
0.049 1
0.8%
0.05 1
0.8%
0.071 1
0.8%
0.09 1
0.8%
0.096 1
0.8%
0.111 1
0.8%
0.113 2
1.7%
0.114 1
0.8%
0.115 1
0.8%
ValueCountFrequency (%)
0.916 1
0.8%
0.835 1
0.8%
0.773 1
0.8%
0.767 1
0.8%
0.752 1
0.8%
0.714 1
0.8%
0.713 1
0.8%
0.712 1
0.8%
0.705 1
0.8%
0.7 2
1.7%

Índice de esgoto tratado referido à água consumida
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct45
Distinct (%)39.1%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.11476522
Minimum0
Maximum1
Zeros69
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:44.608866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0965
95-th percentile0.542
Maximum1
Range1
Interquartile range (IQR)0.0965

Descriptive statistics

Standard deviation0.21465122
Coefficient of variation (CV)1.8703508
Kurtosis4.1831283
Mean0.11476522
Median Absolute Deviation (MAD)0
Skewness2.1196734
Sum13.198
Variance0.046075146
MonotonicityNot monotonic
2025-06-06T00:32:44.787264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 69
58.0%
0.023 2
 
1.7%
0.091 2
 
1.7%
0.08 1
 
0.8%
0.024 1
 
0.8%
0.077 1
 
0.8%
0.064 1
 
0.8%
0.048 1
 
0.8%
0.102 1
 
0.8%
0.522 1
 
0.8%
Other values (35) 35
29.4%
(Missing) 4
 
3.4%
ValueCountFrequency (%)
0 69
58.0%
0.007 1
 
0.8%
0.01 1
 
0.8%
0.013 1
 
0.8%
0.017 1
 
0.8%
0.023 2
 
1.7%
0.024 1
 
0.8%
0.027 1
 
0.8%
0.048 1
 
0.8%
0.049 1
 
0.8%
ValueCountFrequency (%)
1 1
0.8%
0.95 1
0.8%
0.76 1
0.8%
0.645 1
0.8%
0.618 1
0.8%
0.577 1
0.8%
0.527 1
0.8%
0.522 1
0.8%
0.496 1
0.8%
0.49 1
0.8%

Tarifa de água
Real number (ℝ)

Missing 

Distinct98
Distinct (%)93.3%
Missing14
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean3.5944762
Minimum0.01
Maximum7.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:44.954473image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.588
Q12.35
median3.94
Q34.55
95-th percentile6.428
Maximum7.81
Range7.8
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.7227845
Coefficient of variation (CV)0.47928667
Kurtosis-0.17102721
Mean3.5944762
Median Absolute Deviation (MAD)1.12
Skewness-0.052560244
Sum377.42
Variance2.9679865
MonotonicityNot monotonic
2025-06-06T00:32:45.129913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.26 3
 
2.5%
2.63 2
 
1.7%
3.53 2
 
1.7%
2.53 2
 
1.7%
3.38 2
 
1.7%
4.47 2
 
1.7%
4.16 1
 
0.8%
4.49 1
 
0.8%
4.54 1
 
0.8%
4.72 1
 
0.8%
Other values (88) 88
73.9%
(Missing) 14
 
11.8%
ValueCountFrequency (%)
0.01 1
0.8%
0.1 1
0.8%
0.12 1
0.8%
0.16 1
0.8%
0.46 1
0.8%
0.56 1
0.8%
0.7 1
0.8%
0.72 1
0.8%
0.74 1
0.8%
1.1 1
0.8%
ValueCountFrequency (%)
7.81 1
0.8%
7.38 1
0.8%
7.34 1
0.8%
7.14 1
0.8%
7.13 1
0.8%
6.49 1
0.8%
6.18 1
0.8%
6.03 1
0.8%
5.59 1
0.8%
5.42 1
0.8%

Custo com energia elétrica
Real number (ℝ)

High correlation  Missing 

Distinct113
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean4708733.3
Minimum1
Maximum75852326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:45.300871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile89212.792
Q1503308.58
median1478276.5
Q34480972.5
95-th percentile15938562
Maximum75852326
Range75852325
Interquartile range (IQR)3977663.9

Descriptive statistics

Standard deviation10545652
Coefficient of variation (CV)2.2395941
Kurtosis25.263853
Mean4708733.3
Median Absolute Deviation (MAD)1281250.8
Skewness4.7453431
Sum5.3208687 × 108
Variance1.1121077 × 1014
MonotonicityNot monotonic
2025-06-06T00:32:45.476328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5221845.92 1
 
0.8%
950177.56 1
 
0.8%
4003964.65 1
 
0.8%
3598261.39 1
 
0.8%
1287341.31 1
 
0.8%
284583.26 1
 
0.8%
717461.31 1
 
0.8%
527968.37 1
 
0.8%
2305393.49 1
 
0.8%
7328796 1
 
0.8%
Other values (103) 103
86.6%
(Missing) 6
 
5.0%
ValueCountFrequency (%)
1 1
0.8%
10450.77 1
0.8%
14890.46 1
0.8%
35000 1
0.8%
68661.82 1
0.8%
86452 1
0.8%
91053.32 1
0.8%
95088.35 1
0.8%
96841.43 1
0.8%
101015.44 1
0.8%
ValueCountFrequency (%)
75852325.68 1
0.8%
57185383.24 1
0.8%
44588711.4 1
0.8%
38734328.54 1
0.8%
21627015.89 1
0.8%
16258730.43 1
0.8%
15725116.3 1
0.8%
14449422.56 1
0.8%
10123834.68 1
0.8%
10054042.41 1
0.8%
Distinct118
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.866689
Minimum0
Maximum181.703
Zeros2
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:45.645231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3515
Q13.8515
median9.349
Q316.244
95-th percentile58.1715
Maximum181.703
Range181.703
Interquartile range (IQR)12.3925

Descriptive statistics

Standard deviation25.061868
Coefficient of variation (CV)1.4858795
Kurtosis18.288482
Mean16.866689
Median Absolute Deviation (MAD)5.96
Skewness3.8000575
Sum2007.136
Variance628.09722
MonotonicityNot monotonic
2025-06-06T00:32:45.818519image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.7%
26.445 1
 
0.8%
181.703 1
 
0.8%
28.911 1
 
0.8%
6.422 1
 
0.8%
11.164 1
 
0.8%
10.227 1
 
0.8%
4.562 1
 
0.8%
1.538 1
 
0.8%
2.162 1
 
0.8%
Other values (108) 108
90.8%
ValueCountFrequency (%)
0 2
1.7%
0.656 1
0.8%
1.053 1
0.8%
1.273 1
0.8%
1.347 1
0.8%
1.352 1
0.8%
1.423 1
0.8%
1.538 1
0.8%
1.546 1
0.8%
1.573 1
0.8%
ValueCountFrequency (%)
181.703 1
0.8%
105.51 1
0.8%
104.059 1
0.8%
101.623 1
0.8%
86.385 1
0.8%
62.811 1
0.8%
57.656 1
0.8%
55.162 1
0.8%
41.914 1
0.8%
38.562 1
0.8%

Moradias sem banheiro
Real number (ℝ)

High correlation 

Distinct118
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1928.7227
Minimum2
Maximum7803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-06-06T00:32:45.990873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile40.8
Q1313.5
median1319
Q33129
95-th percentile5750.9
Maximum7803
Range7801
Interquartile range (IQR)2815.5

Descriptive statistics

Standard deviation1910.4499
Coefficient of variation (CV)0.99052595
Kurtosis0.59973628
Mean1928.7227
Median Absolute Deviation (MAD)1111
Skewness1.1180232
Sum229518
Variance3649818.7
MonotonicityNot monotonic
2025-06-06T00:32:46.239466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2
 
1.7%
5337 1
 
0.8%
805 1
 
0.8%
3944 1
 
0.8%
2483 1
 
0.8%
3558 1
 
0.8%
6008 1
 
0.8%
243 1
 
0.8%
3083 1
 
0.8%
1869 1
 
0.8%
Other values (108) 108
90.8%
ValueCountFrequency (%)
2 2
1.7%
16 1
0.8%
20 1
0.8%
38 1
0.8%
39 1
0.8%
41 1
0.8%
42 1
0.8%
49 1
0.8%
54 1
0.8%
55 1
0.8%
ValueCountFrequency (%)
7803 1
0.8%
7727 1
0.8%
7083 1
0.8%
6286 1
0.8%
6008 1
0.8%
5768 1
0.8%
5749 1
0.8%
5730 1
0.8%
5614 1
0.8%
5337 1
0.8%

Interactions

2025-06-06T00:32:32.296949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:27.281547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:30.182770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-06-06T00:31:32.833696image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:35.570098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:38.159208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:40.770133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:43.372435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:46.030731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:48.852581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:51.529033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:54.209847image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:56.891827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:31:59.592698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:02.170183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:04.980088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:07.720725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:10.337285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:13.118744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:15.931532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:18.675275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:21.147282image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:23.841574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:26.572298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:29.337283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-06T00:32:32.178760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-06-06T00:32:46.397533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Consumo per capita de águaCusto com energia elétricaDensidade demográficaEstadoIDIncidência de internações por diarreiaIncidência de internações totais por doenças de veiculação hídricaLatitudeLongitudeMoradias sem banheiroParcela da população com acesso à águaParcela da população com coleta de esgotoParcela da população sem acesso à águaParcela da população sem coleta de esgotoParcela das moradias sem banheiroPerdas na distribuiçãoPopulaçãoPopulação com acesso à águaPopulação com coleta de esgotoPopulação sem acesso à águaPopulação sem coleta de esgotoReceita direta e indireta totalTarifa de águaTaxa de óbitos por doenças de veiculação hídricaÁrea do municípioÍndice de esgoto tratado referido à água consumida
Consumo per capita de água1.0000.3960.0420.0000.169-0.257-0.165-0.325-0.100-0.3440.8410.363-0.841-0.363-0.378-0.0870.1040.5620.340-0.642-0.0980.425-0.293-0.1810.0120.260
Custo com energia elétrica0.3961.0000.5260.1840.194-0.272-0.175-0.1460.155-0.0140.4750.621-0.475-0.621-0.4880.3590.7590.8590.6850.0360.5280.8840.1510.057-0.1290.639
Densidade demográfica0.0420.5261.0000.0000.063-0.121-0.1520.3090.4260.0590.1060.384-0.106-0.384-0.2660.1520.5470.4970.4390.2430.4590.5690.2200.179-0.7990.383
Estado0.0000.1840.0001.0000.9350.0000.0000.6220.6240.3100.1670.3160.1420.2750.1250.2360.1330.1470.4000.2230.0760.1870.4370.0000.0000.275
ID0.1690.1940.0630.9351.000-0.263-0.313-0.3780.417-0.3260.1480.226-0.148-0.226-0.2990.087-0.1270.0190.195-0.226-0.2110.2020.142-0.161-0.2080.249
Incidência de internações por diarreia-0.257-0.272-0.1210.000-0.2631.0000.9160.3320.1910.336-0.267-0.4220.2670.4220.464-0.026-0.199-0.308-0.4360.1510.005-0.376-0.0630.3880.055-0.427
Incidência de internações totais por doenças de veiculação hídrica-0.165-0.175-0.1520.000-0.3130.9161.0000.1700.0370.224-0.158-0.3230.1580.3230.3370.003-0.164-0.212-0.3460.092-0.035-0.262-0.0390.3750.126-0.315
Latitude-0.325-0.1460.3090.622-0.3780.3320.1701.0000.3660.569-0.367-0.2770.3670.2770.527-0.1060.111-0.162-0.2360.4430.267-0.258-0.0790.278-0.251-0.291
Longitude-0.1000.1550.4260.6240.4170.1910.0370.3661.0000.230-0.1450.0040.145-0.0040.2060.1130.013-0.0590.0020.1470.1030.0740.1510.210-0.522-0.008
Moradias sem banheiro-0.344-0.0140.0590.310-0.3260.3360.2240.5690.2301.000-0.350-0.2730.3500.2730.7950.2080.276-0.043-0.2080.4910.463-0.197-0.1010.2600.155-0.291
Parcela da população com acesso à água0.8410.4750.1060.1670.148-0.267-0.158-0.367-0.145-0.3501.0000.407-1.000-0.407-0.449-0.0040.1910.6760.391-0.763-0.0420.534-0.122-0.1780.0260.309
Parcela da população com coleta de esgoto0.3630.6210.3840.3160.226-0.422-0.323-0.2770.004-0.2730.4071.000-0.407-1.000-0.5900.1120.5480.6630.984-0.0360.1360.7810.295-0.047-0.1050.901
Parcela da população sem acesso à água-0.841-0.475-0.1060.142-0.1480.2670.1580.3670.1450.350-1.000-0.4071.0000.4070.4490.004-0.191-0.676-0.3910.7630.042-0.5340.1220.178-0.026-0.309
Parcela da população sem coleta de esgoto-0.363-0.621-0.3840.275-0.2260.4220.3230.277-0.0040.273-0.407-1.0000.4071.0000.590-0.112-0.548-0.663-0.9840.036-0.136-0.781-0.2950.0470.105-0.901
Parcela das moradias sem banheiro-0.378-0.488-0.2660.125-0.2990.4640.3370.5270.2060.795-0.449-0.5900.4490.5901.0000.056-0.264-0.497-0.5940.233-0.020-0.667-0.3110.1570.186-0.619
Perdas na distribuição-0.0870.3590.1520.2360.087-0.0260.003-0.1060.1130.208-0.0040.1120.004-0.1120.0561.0000.2370.2080.1520.1420.2140.251-0.0530.056-0.0160.179
População0.1040.7590.5470.133-0.127-0.199-0.1640.1110.0130.2760.1910.548-0.191-0.548-0.2640.2371.0000.7900.6260.3710.8080.7540.1980.169-0.0340.572
População com acesso à água0.5620.8590.4970.1470.019-0.308-0.212-0.162-0.059-0.0430.6760.663-0.676-0.663-0.4970.2080.7901.0000.715-0.1230.5410.8630.0210.025-0.0670.617
População com coleta de esgoto0.3400.6850.4390.4000.195-0.436-0.346-0.2360.002-0.2080.3910.984-0.391-0.984-0.5940.1520.6260.7151.0000.0370.2510.8200.295-0.042-0.1160.897
População sem acesso à água-0.6420.0360.2430.223-0.2260.1510.0920.4430.1470.491-0.763-0.0360.7630.0360.2330.1420.371-0.1230.0371.0000.520-0.0180.1730.293-0.0720.043
População sem coleta de esgoto-0.0980.5280.4590.076-0.2110.005-0.0350.2670.1030.463-0.0420.1360.042-0.136-0.0200.2140.8080.5410.2510.5201.0000.4380.0800.274-0.0610.170
Receita direta e indireta total0.4250.8840.5690.1870.202-0.376-0.262-0.2580.074-0.1970.5340.781-0.534-0.781-0.6670.2510.7540.8630.820-0.0180.4381.0000.3550.055-0.1830.782
Tarifa de água-0.2930.1510.2200.4370.142-0.063-0.039-0.0790.151-0.101-0.1220.2950.122-0.295-0.311-0.0530.1980.0210.2950.1730.0800.3551.0000.113-0.1550.386
Taxa de óbitos por doenças de veiculação hídrica-0.1810.0570.1790.000-0.1610.3880.3750.2780.2100.260-0.178-0.0470.1780.0470.1570.0560.1690.025-0.0420.2930.2740.0550.1131.000-0.094-0.083
Área do município0.012-0.129-0.7990.000-0.2080.0550.126-0.251-0.5220.1550.026-0.105-0.0260.1050.186-0.016-0.034-0.067-0.116-0.072-0.061-0.183-0.155-0.0941.000-0.099
Índice de esgoto tratado referido à água consumida0.2600.6390.3830.2750.249-0.427-0.315-0.291-0.008-0.2910.3090.901-0.309-0.901-0.6190.1790.5720.6170.8970.0430.1700.7820.386-0.083-0.0991.000

Missing values

2025-06-06T00:32:35.191470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-06T00:32:35.629030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-06T00:32:36.028743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CidadeEstadoIDLatitudeLongitudeAmazoniaPopulaçãoPopulação com acesso à águaPopulação sem acesso à águaPopulação com coleta de esgotoPopulação sem coleta de esgotoParcela da população sem acesso à águaParcela da população com acesso à águaParcela da população sem coleta de esgotoParcela da população com coleta de esgotoDensidade demográficaÁrea do municípioReceita direta e indireta totalParcela das moradias sem banheiroIncidência de internações por diarreiaTaxa de óbitos por doenças de veiculação hídricaConsumo per capita de águaPerdas na distribuiçãoÍndice de esgoto tratado referido à água consumidaTarifa de águaCusto com energia elétricaIncidência de internações totais por doenças de veiculação hídricaMoradias sem banheiro
0Cruzeiro do SulAcre120020-7.636248-72.669165Sim91888.046566.045322.00.091888.00.4930.5071.0000.00010.478779.4035295069.280.19964.1350.10960.000.6950.0002.635221845.9226.4455337.0
1Rio BrancoAcre120040-9.976536-67.822078Sim364756.0195152.0169604.075383.0289373.00.4650.5350.7930.20741.298834.94249594663.940.04131.2060.05597.570.5660.0072.9415725116.302.6045116.0
21MacapáAmapá1600300.040153-51.056959Sim442933.0240857.0202076.035660.0407273.00.4560.5440.9190.08168.116503.45858973856.060.02291.8060.00065.700.7140.2223.337663301.092.2802828.0
22SantanaAmapá160060-0.030707-51.178967Sim107618.048518.059100.01415.0106203.00.5490.4510.9870.01369.831541.2249278073.050.03688.5490.27990.820.5840.0002.452220334.479.6641060.0
23Careiro da VárzeaAmazonas130115-3.197024-59.825910Sim19637.0549.019088.00.019637.00.9720.0281.0000.0007.472627.474309691.610.21302.0370.00020.930.7000.0000.5695088.352.0371231.0
24CoariAmazonas130120-4.088596-63.143117Sim70616.011246.059370.00.070616.00.8410.1591.0000.0001.2257970.768909202.000.183012.3200.00015.700.1110.0002.35126250.0016.7103215.0
25HumaitáAmazonas130170-7.509940-63.024867Sim57473.057195.0278.00.057473.00.0050.9951.0000.0001.7433111.1642760297.340.13835.2200.522106.100.6820.0001.241140000.0013.9201994.0
26IrandubaAmazonas130185-3.275820-60.186743Sim61163.035610.025553.00.061163.00.4180.5821.0000.00027.592216.8171578335.720.06329.4830.00069.880.0960.0003.421.0010.9541122.0
27ItacoatiaraAmazonas130190-3.147790-58.446104Sim103598.060856.042742.00.0103598.00.4130.5871.0000.00011.658891.9064985641.460.07756.0810.193333.000.2450.0001.4914890.466.3712143.0
28ManacapuruAmazonas130250-3.299677-60.621353Sim101883.092000.09883.00.0101883.00.0970.9031.0000.00013.897336.5794917433.840.088520.6120.19643.030.0480.0002.56NaN21.1032379.0
CidadeEstadoIDLatitudeLongitudeAmazoniaPopulaçãoPopulação com acesso à águaPopulação sem acesso à águaPopulação com coleta de esgotoPopulação sem coleta de esgotoParcela da população sem acesso à águaParcela da população com acesso à águaParcela da população sem coleta de esgotoParcela da população com coleta de esgotoDensidade demográficaÁrea do municípioReceita direta e indireta totalParcela das moradias sem banheiroIncidência de internações por diarreiaTaxa de óbitos por doenças de veiculação hídricaConsumo per capita de águaPerdas na distribuiçãoÍndice de esgoto tratado referido à água consumidaTarifa de águaCusto com energia elétricaIncidência de internações totais por doenças de veiculação hídricaMoradias sem banheiro
628Ji-ParanáRondônia110012-10.877815-61.927785Sim124333.0100359.023974.01728.0122605.00.1930.8070.9860.01418.036896.6491.995514e+070.00564.5040.000102.290.4790.0133.382228379.7915.362252.0
629Porto VelhoRondônia110020-8.749453-63.873544Sim460434.0192424.0268010.045525.0414909.00.5820.4180.9010.09913.5134090.9625.169978e+070.01271.8030.06544.810.7730.0175.3814449422.569.4261929.0
630Rolim de MouraRondônia110028-11.727071-61.771411Sim56406.046772.09634.01425.054981.00.1710.8290.9750.02538.691457.8881.824448e+070.00186.0280.177149.530.2230.0275.401104561.9115.77838.0
631VilhenaRondônia110030-12.736854-60.146555Sim95832.095599.0233.00.095832.00.0020.9981.0000.0008.1911699.1461.697410e+070.00193.6520.104204.740.6670.0001.863722112.198.87067.0
632Boa VistaRoraima1400102.820848-60.671958Sim413486.0398806.014680.0383731.029755.00.0360.9640.0720.92872.715687.0371.114705e+080.01015.9490.145149.400.5320.9502.8210054042.4115.3091206.0
834AraguaínaTocantins170210-7.193241-48.201860Sim171301.0163091.08210.056218.0115083.00.0480.9520.6720.32842.824000.4169.357636e+070.00686.3050.058148.320.3000.2617.144480972.4812.726393.0
835GurupiTocantins170950-11.727940-49.068046Sim85125.083179.01946.053604.031521.00.0230.9770.3700.63046.361836.0915.533859e+070.00243.7590.235150.240.3010.4247.381953491.546.81473.0
836PalmasTocantins172100-10.183785-48.333642Sim302692.0296432.06260.0272301.030391.00.0210.9790.1000.900136.412218.9422.479496e+080.00230.6940.000158.590.3170.6457.8110123834.683.205244.0
837Paraíso do TocantinsTocantins171610-10.175247-48.886759Sim52360.050647.01713.013841.038519.00.0330.9670.7360.26441.291268.0602.571877e+070.00302.1010.191139.410.2620.1517.131420308.229.16755.0
838Porto NacionalTocantins171820-10.701979-48.411095Sim64418.055622.08796.042089.022329.00.1370.8630.3470.65314.484449.9173.972704e+070.00756.3650.155129.970.3910.4907.342297309.8814.747161.0